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                    <p id="main-toc"><strong>目录</strong></p> 
<p id="1%E3%80%81rnn%E6%98%AF%E4%BB%80%E4%B9%88-toc" style="margin-left:40px;"><a href="#1%E3%80%81rnn%E6%98%AF%E4%BB%80%E4%B9%88" title="1、rnn是什么">1、rnn是什么</a></p> 
<p id="2%E3%80%81%E5%8E%9F%E7%90%86%E8%AF%B4%E6%98%8E-toc" style="margin-left:40px;"><a href="#2%E3%80%81%E5%8E%9F%E7%90%86%E8%AF%B4%E6%98%8E" title="2、原理说明">2、原理说明</a></p> 
<p id="2.1%20rnn%20%E5%92%8C%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%95%A5%E7%9A%84%E5%8C%BA%E5%88%AB-toc" style="margin-left:80px;"><a href="#2.1%20rnn%20%E5%92%8C%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%95%A5%E7%9A%84%E5%8C%BA%E5%88%AB" title="2.1 rnn 和全连接神经网略的区别">2.1 rnn 和全连接神经网略的区别</a></p> 
<p id="2.2%20RNN%20%E7%AE%80%E5%8D%95%E5%9B%BE%E8%A7%A3%E9%87%8A-toc" style="margin-left:80px;"><a href="#2.2%20RNN%20%E7%AE%80%E5%8D%95%E5%9B%BE%E8%A7%A3%E9%87%8A" title="2.2 RNN 简单图解释">2.2 RNN 简单图解释</a></p> 
<p id="2.3%20RNN%E5%B1%95%E5%BC%80%E5%9B%BE%E8%A7%A3%E9%87%8A-toc" style="margin-left:80px;"><a href="#2.3%20RNN%E5%B1%95%E5%BC%80%E5%9B%BE%E8%A7%A3%E9%87%8A" title="2.3 RNN展开图解释">2.3 RNN展开图解释</a></p> 
<p id="2.4%20RNN%E7%9A%84%E4%B8%80%E4%BA%9B%E7%82%B9-toc" style="margin-left:80px;"><a href="#2.4%20RNN%E7%9A%84%E4%B8%80%E4%BA%9B%E7%82%B9" title="2.4 RNN的一些点">2.4 RNN的一些点</a></p> 
<p id="3%E3%80%81rnn%E7%9A%84%E4%BC%AA%E4%BB%A3%E7%A0%81%E8%A1%A8%E7%A4%BA-toc" style="margin-left:40px;"><a href="#3%E3%80%81rnn%E7%9A%84%E4%BC%AA%E4%BB%A3%E7%A0%81%E8%A1%A8%E7%A4%BA" title="3、rnn的伪代码表示">3、rnn的伪代码表示</a></p> 
<p id="4%E3%80%81%E6%9D%A5%E4%B8%AA%E5%B0%8F%E4%BE%8B%E5%AD%90-toc" style="margin-left:40px;"><a href="#4%E3%80%81%E6%9D%A5%E4%B8%AA%E5%B0%8F%E4%BE%8B%E5%AD%90" title="4、来个小例子">4、来个小例子</a></p> 
<p id="5%E3%80%81rnn%E5%AD%98%E5%9C%A8%E7%9A%84%E9%97%AE%E9%A2%98-toc" style="margin-left:40px;"><a href="#5%E3%80%81rnn%E5%AD%98%E5%9C%A8%E7%9A%84%E9%97%AE%E9%A2%98" title="5、rnn存在的问题">5、rnn存在的问题</a></p> 
<p id="6%E3%80%81%E6%80%BB%E7%BB%93-toc" style="margin-left:40px;"><a href="#6%E3%80%81%E6%80%BB%E7%BB%93" title="6、总结">6、总结</a></p> 
<hr id="hr-toc" />
<p>最近写了一些基础的东西&#xff0c;总是理解性的&#xff0c;没有看到实例&#xff0c;今天就讲一个基础的网络结构RNN&#xff0c;然后写个实例&#xff0c;体验下深度神经网络的牛逼&#xff0c;这次学习下rnn神经网络&#xff0c;虽然看起来好高深&#xff0c;不过不用慌&#xff0c;没有理论&#xff0c;全是大白话&#xff0c;大家都可以懂的。</p> 
<p>注&#xff1a;阅读的时候希望先看下目录&#xff0c;知道我在说什么&#xff0c;也可以快速的阅读到自己想get的点&#xff0c;这样阅读能更快的理解。</p> 
<h2 id="1%E3%80%81rnn%E6%98%AF%E4%BB%80%E4%B9%88">1、rnn是什么</h2> 
<p>RNN 是循环神经网络的简称&#xff0c;他的英语是 Rerrent Neural Network &#61; RNN&#xff0c;从命名中可以看到核心点是循环的神经网络&#xff0c;所以我们要理解循环的是什么&#xff1f;为什么要循环。这个会慢慢解释。不用着急。</p> 
<p><strong>RNN对具有序列特性的数据非常有效&#xff0c;它能挖掘数据中的时序信息以及语义信息</strong>&#xff0c;用人话解释就是RNN具有语境信息&#xff0c;可以理解上下文&#xff0c;在你进行分析的时候可以对全局进行考虑&#xff0c;可以用来挖掘数据之间的关系。</p> 
<p>举个例子&#xff1a;我不喜欢美女&#xff0c;对文字进行拆分&#xff0c;我&#xff0c;不&#xff0c;喜欢&#xff0c;美女&#xff0c;在普通的全连接的神经网络中会每个词是鼓励的&#xff0c;没有关系的&#xff0c;通过大量的函数去拟合数据&#xff0c;这个时候机器可能理解的就是喜欢美女&#xff0c;没有考虑到前面的“不”带来的语境信息。</p> 
<p>RNN 可以解决这个问题&#xff0c;RNN 会记录整个句子出现的信息&#xff0c;然后进行综合评判&#xff0c;最后才得出结论。</p> 
<p>总结下&#xff1a;RNN神经网络善于发现数据之间的关系。</p> 
<h2 id="2%E3%80%81%E5%8E%9F%E7%90%86%E8%AF%B4%E6%98%8E">2、原理说明</h2> 
<h3 id="2.1%20rnn%20%E5%92%8C%E5%85%A8%E8%BF%9E%E6%8E%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%95%A5%E7%9A%84%E5%8C%BA%E5%88%AB">2.1 rnn 和全连接神经网略的区别</h3> 
<p>普通的全连接的神经网络像下面这样&#xff0c;每个属性是独立的&#xff0c;然后通过大量的函数参数进行拟合&#xff0c;然后进行处理&#xff0c;得出自己的结论&#xff0c;就像上篇入门的时候对函数的拟合&#xff0c;可以看出每个数据之间并没有什么关联</p> 
<p><img alt="" height="1" src="https://img-blog.csdnimg.cn/20210915094621967.gif" width="1" /></p> 
<p style="text-align:center;"><img alt="图片" src="https://img-blog.csdnimg.cn/img_convert/9a5878e4e967a02e853c85c96c0d7bfa.png" /></p> 
<p>再看看我们今天说的RNN&#xff0c;循环的神经网络&#xff0c;注&#xff1a;这张图来自百度&#xff0c;我为什么要展示这张图呐&#xff1f;虽然我想用最直白的话给描述RNN&#xff0c;但是以后你可能会查阅资料&#xff0c;会频繁的看到这张图&#xff0c;所以我贴进来&#xff0c;以免下次遇到的时候没办法理解&#xff0c;因为这个图理解起来还是不那么好理解&#xff0c;当然如果你理解了RNN可能会明白。但是对于入门的我们来说还是有点费劲的。</p> 
<p style="text-align:center;"><img alt="图片" src="https://img-blog.csdnimg.cn/img_convert/0c9d149deafa862b5109360e56777bd5.png" /></p> 
<h3 id="2.2%20RNN%20%E7%AE%80%E5%8D%95%E5%9B%BE%E8%A7%A3%E9%87%8A">2.2 RNN 简单图解释</h3> 
<p>左边的部分是没展开的RNN的见图&#xff0c;到这里可以看到循环神经网络的循环在哪里了</p> 
<p>x是一个向量&#xff0c;它表示<strong>输入层</strong>的值</p> 
<p>U是输入层到隐藏层的<strong>权重矩阵</strong></p> 
<p>s是一个向量&#xff0c;它表示<strong>隐藏层</strong>的值</p> 
<p>V是隐藏层到输出层的<strong>权重矩阵</strong>。</p> 
<p>o也是一个向量&#xff0c;它表示<strong>输出层</strong>的值</p> 
<p>用函数表示 &#xff1a;</p> 
<p style="text-align:center;"><img alt="图片" src="https://img-blog.csdnimg.cn/img_convert/dc025c7eb5061eb11fb4bf4019e6522d.png" /></p> 
<p></p> 
<p>用代码简单的理解下</p> 
<pre><code class="language-python">def getHidenS(x,w,prevS):
        return x * u &#43; prevS*w
def getOutput(s):
        return s * v</code></pre> 
<h3 id="2.3%20RNN%E5%B1%95%E5%BC%80%E5%9B%BE%E8%A7%A3%E9%87%8A">2.3 RNN展开图解释</h3> 
<p>右边的图看起来很简单&#xff0c;但是下面的 x 增加了时间序列&#xff0c;这里的x 表示时间上的输入单词&#xff0c;举个例子&#xff1a;</p> 
<p>比如&#xff1a;我爱中国&#xff0c;这个词序列&#xff0c;t-1 就是我 这个词的向量表示&#xff0c;爱 就是 t 这个词的向量表示&#xff0c;t&#43;1 就是中国这个词的向量表示</p> 
<p>o就是在输入每个单词时神经网络的输出&#xff0c;也就是说每次输入一个词向量的时候都会有一个输出&#xff0c;最后得出结论可以使用其中一个&#xff0c;或者综合考虑都可以&#xff0c;根据自己的需求</p> 
<h3 id="2.4%20RNN%E7%9A%84%E4%B8%80%E4%BA%9B%E7%82%B9">2.4 RNN的一些点</h3> 
<p>公用参数&#xff1a;RNN的时间序列公用参数&#xff0c;也就是说整个RNN公用一组参数&#xff0c;不同的时间点的输入的时候&#xff0c;神经网络的权重参数都是一组。也就是说只有一组W参数。</p> 
<p>具有记忆功能&#xff1a;记忆功能的实现是基于隐藏层的输出值实现的。因为隐藏层会将上一次的信息进行保存</p> 
<h2 id="3%E3%80%81rnn%E7%9A%84%E4%BC%AA%E4%BB%A3%E7%A0%81%E8%A1%A8%E7%A4%BA">3、rnn的伪代码表示</h2> 
<p>对”我爱中国“进行编码&#xff0c;我&#61; 1 爱 &#61; 2 中国&#61;3</p> 
<p>输入 x &#61; [1,2,3]</p> 
<pre><code class="language-python">w &#61; 1 # 权重矩阵
u &#61; 1 # 输入层到隐藏层的矩阵
v &#61; 2 # 隐藏层到输出层的矩阵
prevS &#61; 1 # 隐藏层的输出值


def getHidenS(x, w, prevS):
   return x * u &#43; prevS * w

def getOutput(s,v):
   return s * v

sentance &#61; [1,2,3]
for x in sentance:
   prevS &#61; getHidenS(x,u,prevS)
   o &#61; getOutput(prevS,v)
   print(&#39;隐藏层的值&#xff1a;&#39;&#43; str(prevS))
   print(&#39;输出层的值 &#xff1a;&#39;&#43;str(o))
   print(&#39;----------------------&#39;)</code></pre> 
<p>prevS 保存了之前的记忆&#xff0c;每一次的输出都可以用来判断</p> 
<p style="text-align:center;"><img alt="图片" src="https://img-blog.csdnimg.cn/img_convert/3a9c191627610c6bf5fc9359d522e462.png" /></p> 
<h2 id="4%E3%80%81%E6%9D%A5%E4%B8%AA%E5%B0%8F%E4%BE%8B%E5%AD%90">4、来个小例子</h2> 
<pre><code class="language-python">import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt

# https://www.cnblogs.com/lokvahkoor/p/12263953.html
# torch.manual_seed(1)    # reproducible

# 超参数定义
TIME_STEP &#61; 10  # rnn time step
INPUT_SIZE &#61; 1  # rnn input size
LR &#61; 0.02  # 学习率
HIDDEN_SIZE &#61; 32# 隐藏层神经元个数
EPOCH &#61; 100

# 横向坐标&#xff0c;产出100个float点
steps &#61; np.linspace(0, np.pi * 2, 100, dtype&#61;np.float32)  # float32 for converting torch FloatTensor
x_np &#61; np.sin(steps)
y_np &#61; np.cos(steps)
# 输入的参数是sin的序列&#xff0c;输出是余弦序列
plt.plot(steps, y_np, &#39;r-&#39;, label&#61;&#39;target (cos)&#39;)
plt.plot(steps, x_np, &#39;b-&#39;, label&#61;&#39;input (sin)&#39;)
plt.legend(loc&#61;&#39;best&#39;)
plt.show()

input(&#34;请回车&#xff1a;&#34;)

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()

        self.rnn &#61; nn.RNN(
            input_size&#61;INPUT_SIZE,
            hidden_size&#61;HIDDEN_SIZE,  # 隐藏神经元的数量
            num_layers&#61;1,  # 一层rnn
            batch_first&#61;True,  # input &amp; output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
        )
        self.out &#61; nn.Linear(HIDDEN_SIZE, 1)

    def forward(self, x, h_state):
        # x &#61; (batch, time_step, input_size)
        # h_state &#61; (n_layers, batch, hidden_size)
        # r_out &#61; (batch, time_step, hidden_size)

        out, h_state &#61; self.rnn(x, h_state)

        out &#61; out.view(-1, HIDDEN_SIZE) #(10,32)
        out &#61; self.out(out) #(10,1)
        out &#61; out.unsqueeze(dim&#61;0) # (1,10,1) -&gt; (n_layers, batch, hidden_size)
        return out, h_state


rnn &#61; RNN()
print(rnn)

optimizer &#61; torch.optim.Adam(rnn.parameters(), lr&#61;LR)  # 优化器
loss_func &#61; nn.MSELoss()  # 损失函数

h_state &#61; None  # 隐藏层的输出值

plt.figure(1, figsize&#61;(12, 5))
plt.ion()  # continuously plot

for step in range(EPOCH):
    # 每次生成新的数据&#xff0c;整体的趋势是拟合成cos函数曲线
    start, end &#61; step * np.pi, (step &#43; 1) * np.pi  # time range
    # use sin predicts cos
    steps &#61; np.linspace(start, end, TIME_STEP, dtype&#61;np.float32,
                        endpoint&#61;False)  # float32 for converting torch FloatTensor
    x_np &#61; np.sin(steps)
    y_np &#61; np.cos(steps)
    # np.newaxis的功能:插入新维度,(1,10,1)
    # shape (batch, time_step, input_size)
    # 表示每一批送进去一个
    x &#61; torch.from_numpy(x_np[np.newaxis, :, np.newaxis])
    y &#61; torch.from_numpy(y_np[np.newaxis, :, np.newaxis])

    prediction, h_state &#61; rnn(x, h_state)  # 计算输出
    # 将上一步的隐藏层的结果进行保存&#xff0c;在下次输入
    h_state &#61; h_state.data  # repack the hidden state, break the connection from last iteration

    loss &#61; loss_func(prediction, y)  # 计算误差
    optimizer.zero_grad()  # 清除之前的梯度
    loss.backward()  # 反向传播
    optimizer.step()  # 优化参数

    # 开始画图
    plt.plot(steps, y_np.flatten(), &#39;r-&#39;)
    plt.plot(steps, prediction.data.numpy().flatten(), &#39;b-&#39;)
    plt.draw()
    plt.pause(0.05)

plt.ioff()
plt.show()</code></pre> 
<p>看一下最后的拟合结果&#xff1a;</p> 
<p><img alt="" height="1" src="https://img-blog.csdnimg.cn/20210915094621963.gif" width="1" /></p> 
<p style="text-align:center;"><img alt="图片" src="https://img-blog.csdnimg.cn/img_convert/a148aa87b4236792261c38fb4b96043e.png" /></p> 
<h2 id="5%E3%80%81rnn%E5%AD%98%E5%9C%A8%E7%9A%84%E9%97%AE%E9%A2%98">5、rnn存在的问题</h2> 
<p>对于梯度消失: 由于它们都有特殊的方式存储”记忆”&#xff0c;那么以前梯度比较大的”记忆”不会像简单的RNN一样马上被抹除&#xff0c;因此可以一定程度上克服梯度消失问题。</p> 
<p>对于梯度爆炸:用来克服梯度爆炸的问题就是gradient clipping&#xff0c;也就是当你计算的梯度超过阈值c或者小于阈值-c的时候&#xff0c;便把此时的梯度设置成c或-c。</p> 
<p></p> 
<h2 id="6%E3%80%81%E6%80%BB%E7%BB%93">6、总结</h2> 
<p>RNN的关键点是记忆功能&#xff0c;也就是保存了语境信息&#xff0c;但是也存在一些问题&#xff0c;后面我们会分析怎么解决这个问题。</p> 
<p>原创不容易&#xff0c;求点赞支持&#xff0c;为爱发电。</p> 
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我们先来看一个<em>RNN</em>经典结构，图1展示了一个典型按时间展开后的<em>RNN</em>结构。



从图1可以看出，<em>RNN</em>在每一个时刻都有一个输入Xt，然后根据当前节点的状态At计算输出值ht，而At是根据上一时刻的状态At<em>-</em>1和当前的输入Xt共同决定的。和卷积<em>神经网络</em>卷积核或池化核的参数共享类似，这里<em>RNN</em>结构中的参数在不同时刻中也是共享的。

1.2.1 <em>RNN</em>前向传播过程



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近年来，股票预测还处于一个很热门的阶段，因为股票市场的波动十分巨大，随时可能因为一些新的政策或者其他原因，进行大幅度的波动，导致自然人股民很难对股票进行投资盈利。因此本文想利用现有的模型与算法，对股票价格进行预测，从而使自然人股民可以自己对股票进行预测。
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				<div class="desc ellipsis-online ellipsis-online-1">1.<em>RNN</em>的应用
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <em>RNN</em>主要有两个应用，一是评测一个句子出现的可能性，二是文本自动生成。
2.什么是<em>RNN</em>？
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <em>RNN</em>之所以叫<em>RNN</em>是因为它循环处理相同的任务，就是预测句子接下来的单词是什么。<em>RNN</em>认为在这循环的任务中，各个步骤之间不是独立，于是它循环记录前面所有文本的信息（也叫记忆），作为预测当前词的一个输入。
&nbsp;&nbsp;&nbsp;

在<em>RNN</em>中，每个词作为一层，</div>
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				<div class="desc ellipsis-online ellipsis-online-1">一、前述

CNN和<em>RNN</em>几乎占据着<em>深度学习</em>的半壁江山，所以本文将着重讲解CNN+<em>RNN</em>的各种组合方式，以及CNN和<em>RNN</em>的对比。

二、CNN与<em>RNN</em>对比

1、CNN卷积<em>神经网络</em>与<em>RNN</em>递归<em>神经网络</em>直观图



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				<div class="desc ellipsis-online ellipsis-online-1">Tensorflow2.0全套课程<em>学</em>习笔记！ 

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				<div class="desc ellipsis-online ellipsis-online-1">很多人都有误解，以为<em>深度学习</em>比机器<em>学</em>习先进。其实<em>深度学习</em>是机器<em>学</em>习的一个分支。可以理解为具有多层结构的模型。具体的话，<em>深度学习</em>是机器<em>学</em>习中的具有深层结构的<em>神经网络</em>算法，即机器<em>学</em>习&gt;<em>神经网络</em>算法&gt;深度<em>神经网络</em>（<em>深度学习</em>）。 
关于<em>深度学习</em>的理论推导，太大太复杂，一些常见的<em>深度学习</em>算法本人也是模模糊糊的，看过好多次的，隔断时间就会忘记，现在对其系统的整理一下（从历史，致命问题出发，再看具体算法的思想，</div>
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				<div class="desc ellipsis-online ellipsis-online-1">人类并不是每时每刻都从一片空白的大脑开始他们的思考。在你阅读这篇<em>文章</em>时候，你都是基于自己已经拥有的对先前所见词的理解来推断当前词的真实含义。我们不会将所有的东西都全部丢弃，然后用空白的大脑进行思考。传统的<em>神经网络</em>并不能做到这点，所以采用<em>RNN</em>来解决这一问题。<em>RNN</em> 是包含循环的网络，允许信息的持久化。

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				<div class="desc ellipsis-online ellipsis-online-1">今天开始<em>深度学习</em>的<em>最</em>后一个重量级的<em>神经网络</em>即<em>RNN</em>，这个网络在<em>自然语言处理</em>中用处很大，因此需要掌握它，同时本人打算在<em>深度学习</em>总结完成以后就开始<em>自然语言处理</em>的总结，至于强化<em>学</em>习呢，目前不打算总结了，因为我需要实战已经总结完成的算法，尤其是<em>深度学习</em>和自然语言的处理的实战，所以大方向就这样计划。下面讲讲本节的内容，本节的的内容和以前一样，从<em>最</em>初开始，慢慢探索到LSTM，废话不多说下面开始：

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                    <a href="https://blog.csdn.net/jianpengxuexikaifa" class="user-name" target="_blank">程序员漫话编程: </a>
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                <a class="title text-truncate" target="_blank" href="https://gamwatcher.blog.csdn.net/article/details/120302281#comments_18345392" data-report-click='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://gamwatcher.blog.csdn.net/article/details/120302281#comments_18345392","ab":"new"}' data-report-view='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://gamwatcher.blog.csdn.net/article/details/120302281#comments_18345392","ab":"new"}'>本科生学深度学习-史上最容易懂的RNN文章，小白也能看得懂，评论继续送书</a>
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                    <a href="https://gamwatcher.blog.csdn.net" class="user-name" target="_blank">香菜聊游戏: </a>
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